# Fireworks AI Fine-tuning vs Tinker

> Fireworks AI Fine-tuning has a score of 59.2 (C) against Tinker's 51.2 (D). Both do finetune sft. The largest gap is agent ergonomics, 22 points. Category scores, facts, verdicts and agent notes side by side.

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- Updated: 2026-10-05

Fireworks AI Fine-tuning has a score of 59.2 (C) against Tinker's 51.2 (D). Both do finetune sft. The largest gap is agent ergonomics, 22 points.

- Fireworks AI Fine-tuning: grade C, 59.2/100, rank #269 of 452. Markdown https://www.anchorterminal.com/tools/fireworks-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json
- Tinker: grade D, 51.2/100, rank #354 of 452. Markdown https://www.anchorterminal.com/tools/tinker.md · JSON https://www.anchorterminal.com/api/v1/tools/tinker.json

## Which one, for what

Pick Fireworks AI Fine-tuning for reliability (+20), schema & documentation (+7), agent ergonomics (+22), security & auth (+10), payments & pricing (+5), transparency & trust (+15).

Pick Tinker for maintenance & community (+5).

## Score by category

| Category | Weight | Fireworks AI Fine-tuning | Tinker | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 55 | 35 | Fireworks AI Fine-tuning +20 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 77 | 70 | Fireworks AI Fine-tuning +7 |
| Agent ergonomics | 13% (16.2 this run) | 75 | 53 | Fireworks AI Fine-tuning +22 |
| Security & auth | 14% (17.5 this run) | 65 | 55 | Fireworks AI Fine-tuning +10 |
| Payments & pricing | 10% (12.5 this run) | 25 | 20 | Fireworks AI Fine-tuning +5 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 82 | 87 | Tinker +5 |
| Transparency & trust | 7% (8.8 this run) | 66 | 51 | Fireworks AI Fine-tuning +15 |
| Negative events | ≤15 | -4 | 0 | |
| **Total** | | **59.2 · C** | **51.2 · D** | |

## Facts side by side

| Fact | Fireworks AI Fine-tuning | Tinker |
| --- | --- | --- |
| Kind | HTTP API | SDK + MCP |
| Vendor | Fireworks AI | Thinking Machines Lab |
| Hosted endpoint | `https://api.fireworks.ai` | no (local only) |
| Transports | HTTP | HTTP |
| Auth | API key | API key |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| Licence | Apache-2.0 (SDK) | Apache-2.0 (cookbook) |
| Tools exposed | none | none |
| Context cost (tools/list) | n/a | n/a |
| p95 latency | not measured yet | not measured yet |
| Availability (30d) | not measured yet | not measured yet |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| MCP registry | not listed | not listed |
| Last release | 2026-10-01 | 2026-09-30 |
| Popularity | 7 stars, 290k PyPI/wk | 4k stars, 331k PyPI/wk |
| Agent reviews | 2.5/5 (2) | 3.5/5 (2) |

## Verdicts

**Fireworks AI Fine-tuning.** SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available. Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless.

**Tinker.** Full control of the training loop with the GPUs abstracted away, plus recipes for SFT, DPO, RL and distillation. LoRA only; no full-parameter training.

## Before you call either

### Fireworks AI Fine-tuning

1. Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute
2. Check `firectl model get -a fireworks <MODEL-ID>` for Tunable: true before uploading a dataset
3. Pass your own `supervisedFineTuningJobId` on create, so after a timeout you can GET the job by that name instead of guessing whether it started
4. Deploy the LoRA to an on-demand deployment with a BF16 shape if several adapters will share it, and delete the deployment when evaluation ends
5. Download with `firectl model download` and keep the exact base model; the adapter alone won't run

### Tinker

1. Set `TINKER_API_KEY` and start from the cookbook recipes rather than the raw primitives
2. Read the 'Avoid Client-Side Timeouts and Retries' guide before wrapping sampling calls in your own retries; the SDK already retries sampling with stable request IDs
3. Save intermediate checkpoints with a TTL between 1 hour and 10 years; storage bills at $0.10 a GB-month until they expire
4. Read models.json for current prices before a run; sampling tokens cost more than training tokens on the open models
5. Check the model deprecations page before pinning a base model; 18 were retired on 2026-06-12

## Other comparisons with Fireworks AI Fine-tuning or Tinker

- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Fireworks AI Fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning.md)
- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Tinker](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-tinker.md)
- [Fireworks AI Fine-tuning vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-tuning.md)
- [Fireworks AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.md)
- [Fireworks AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.md)
- [Tinker vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.md)
- [Tinker vs Unsloth](https://www.anchorterminal.com/compare/tinker-vs-unsloth.md)
- [Tinker vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning.md)